Automated Prompt Engineering With DSPy And Intel oneAPI
Prompt engineering helps teach Large Language Models (LLMs) to produce task-specific responses
LLM’s performance on the assigned task, the automated prompt engineering frameworks will then manage the prompt changes
Structure and modularity in DSPy make LLM prompting easier to modify while keeping robustness compared to pure text prompts
It can use Python type to specify the correct multiple-choice response to the question, which It know is the correct answer for the LLM
The LLM will be loaded using llama-cpp-python, a Python wrapper for llama.cpp, once it have chosen which LLM to use
The code sample uses the Intel oneAPI DPC++/C++ Compiler to develop llama-cpp-python with the SYCL backend, enabling LLMs to run on Intel GPUs
The input and output for the LLM will be represented by a module that to create using the Module class from dspy.
To accept a dataset and metric and begin the evaluation process, use the evaluate function
The MIPROv2 will be used to identify more effective LLM prompts, it is an optimizer that uses quick engineering
If you require more customization for your LLM than automated prompt engineering can provide, it recommend exploring our RAG and fine-tuning tools
Lastly, it will show the LLM’s accuracy prior to and following prompt engineering
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